A Visual Analytics Approach for Patient Stratification and Biomarker Discovery
| dc.contributor.author | Alemzadeh, Shiva | en_US |
| dc.contributor.author | Kromp, Florian | en_US |
| dc.contributor.author | Preim, Bernhard | en_US |
| dc.contributor.author | Taschner-Mandl, Sabine | en_US |
| dc.contributor.author | Bühler, Katja | en_US |
| dc.contributor.editor | Kozlíková, Barbora and Linsen, Lars and Vázquez, Pere-Pau and Lawonn, Kai and Raidou, Renata Georgia | en_US |
| dc.date.accessioned | 2019-09-03T13:49:07Z | |
| dc.date.available | 2019-09-03T13:49:07Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | We introduce discoVA as a visual analytics tool for the refinement of risk stratification of cancer patients and biomarker discovery. Currently, tools for the joint analysis of multiple biological and clinical information in this field are insufficient or lacking. Our tool fills this gap by enabling bio-medical experts to explore datasets of cancer patient cohorts. By using multiple coordinated visualization techniques, nested visual queries on various data types can be performed to generate/prove a hypothesis by identifying discrete sub-cohorts. We demonstrated the utility of discoVA by a case study involving bio-medical researchers. | en_US |
| dc.description.sectionheaders | Visual Analytics in Medicine and Biology | |
| dc.description.seriesinformation | Eurographics Workshop on Visual Computing for Biology and Medicine | |
| dc.identifier.doi | 10.2312/vcbm.20191235 | |
| dc.identifier.isbn | 978-3-03868-081-9 | |
| dc.identifier.issn | 2070-5786 | |
| dc.identifier.pages | 91-95 | |
| dc.identifier.uri | https://doi.org/10.2312/vcbm.20191235 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/vcbm20191235 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.subject | I.3.8 [Computer Graphics] | |
| dc.subject | Applications | |
| dc.title | A Visual Analytics Approach for Patient Stratification and Biomarker Discovery | en_US |
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